Method and apparatus for computer vision based attenuation map generation

ABSTRACT

A method of imaging includes obtaining projection data for an object representing an intensity of radiation detected along a plurality of rays through the object, obtaining an outline of the object via a secondary imaging system, the secondary imaging system using non-ionizing radiation, determining, based on the outline, a model and model parameters for the object, calculating, based on the model and the model parameters, a volumetric attenuation map for the object, and reconstructing, based on the projection data and the volumetric attenuation map, an attenuation-corrected volumetric image.

FIELD OF THE INVENTION

This disclosure relates to an imaging apparatus for tomographic imagereconstruction based on obtained projection data and an obtainedattenuation map of an object, the attenuation map of the object beingobtained via a secondary imaging system included in the imagingapparatus, such as optical, infrared, or range-finding devices.

BACKGROUND

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent the work is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

In emission tomography, knowledge of a scanned object's attenuation isused to accurately and quantitatively reconstruct the image. Without theattenuation map, the reconstructed image will exhibit artefacts thatmake the reconstruction harder to interpret. Formerly, knowledge of theobject's attenuation is obtained from either a transmission scan using aradioisotope that revolved around the object, or, in more modern scannertopologies, from a CT (or MR) scan of the object.

Computed tomography (CT) and magnetic resonance (MR) systems and methodsare widely used, particularly for medical imaging and diagnosis. CTsystems generally create projection images of one or more sectionalslices through a subject's body. A radiation source, such as an X-raysource, irradiates the body from one side. A collimator, generallyadjacent to the X-ray source, limits the angular extent of the X-raybeam, so that radiation impinging on the body is substantially confinedto a planar region (i.e., an X-ray projection plane) defining across-sectional slice of the body. At least one detector (and generallymany more than one detector) on the opposite side of the body receivesradiation transmitted through the body in the projection plane. Theattenuation of the radiation that has passed through the body ismeasured by processing electrical signals received from the detector. Insome implementations a multi slice detector configuration is used,providing a volumetric projection of the body rather than planarprojections.

Typically the X-ray source is mounted on a gantry that revolves about along axis of the body. The detectors are likewise mounted on the gantry,opposite the X-ray source. A cross-sectional image of the body isobtained by taking projective attenuation measurements at a series ofgantry rotation angles, transmitting the projection data/sinogram to aprocessor via the slip ring that is arranged between a gantry rotor andstator, and then processing the projection data using a CTreconstruction algorithm (e.g., inverse Radon transform, a filteredback-projection, Feldkamp-based cone-beam reconstruction, iterativereconstruction, or other method). For example, the reconstructed imagecan be a digital CT image that is a square matrix of elements (pixels),each of which represents a volume element (a volume pixel or voxel) ofthe patient's body. In some CT systems, the combination of translationof the body and the rotation of the gantry relative to the body is suchthat the X-ray source traverses a spiral or helical trajectory withrespect to the body. The multiple views are then used to reconstruct aCT image showing the internal structure of the slice or of multiple suchslices.

In some cases, obtaining an attenuation map can prove difficult.Examples of such cases include when the CT (or MR) system may not beinstalled, available, or operational, when the CT scan may impartadditional undesirable radiation dose, and when the CT field of view(FOV) may not cover the entire object being scanned, resulting intruncation artefacts. In these cases, where the attenuation image is notavailable, accurate emission tomographic reconstruction can bedifficult. Thus, analytical methods used to generate an attenuation mapusing the provided features in the CT system or simple additions to thesystem are desired.

SUMMARY

The present disclosure relates to an imaging apparatus, including:processing circuitry configured to obtain projection data for an objectrepresenting an intensity of radiation detected along a plurality ofrays through the object, obtain an outline of the object via a secondaryimaging system, the secondary imaging system using non-ionizingradiation determine, based on the outline, a model and model parametersfor the object, calculate, based on the model and the model parameters,a volumetric attenuation map for the object, and reconstruct, based onthe projection data and the volumetric attenuation map, anattenuation-corrected volumetric image.

The disclosure additionally relates to a method of imaging, including:obtaining projection data for an object representing an intensity ofradiation detected along a plurality of rays through the object,obtaining an outline of the object via a secondary imaging system, thesecondary imaging system using non-ionizing radiation, determining,based on the outline, a model and model parameters for the object,calculating, based on the model and the model parameters, a volumetricattenuation map for the object, and reconstructing, based on theprojection data and the volumetric attenuation map, anattenuation-corrected volumetric image.

Note that this summary section does not specify every embodiment and/orincrementally novel aspect of the present disclosure or claimedinvention. Instead, this summary only provides a preliminary discussionof different embodiments and corresponding points of novelty. Foradditional details and/or possible perspectives of the invention andembodiments, the reader is directed to the Detailed Description sectionand corresponding figures of the present disclosure as further discussedbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of this disclosure that are proposed as exampleswill be described in detail with reference to the following figures,wherein like numerals reference like elements, and wherein:

FIG. 1 shows example reconstructions for an object.

FIG. 2 shows a block diagram of an imaging apparatus, according to anembodiment of the present disclosure.

FIG. 3A shows a solid phantom being outlined, according to an embodimentof the present disclosure.

FIG. 3B shows a transparent phantom 305 being outlined, according to anembodiment of the present disclosure.

FIG. 4 shows an example of different size humans generated with XCAT,according to an embodiment of the present disclosure.

FIG. 5 shows a non-limiting example of a flow chart for a method ofgenerating a volumetric image corrected for attenuation, according to anembodiment of the present disclosure.

FIG. 6A shows a FOV for a CT scanner, according to an embodiment of thepresent disclosure.

FIG. 6B shows a FOV for a PET scanner, according to an embodiment of thepresent disclosure.

FIG. 6C shows an object in a PET/CT scanner with an object outside thescanner's FOV, according to an embodiment of the present disclosure.

FIG. 7 shows a non-limiting example of a flow chart for a method ofgenerating a volumetric image corrected for attenuation with truncated3D volumetric attenuation data, according to an embodiment of thepresent disclosure.

FIG. 8 shows a schematic of an implementation of a CT scanner, accordingto an exemplary embodiment of the present disclosure.

FIG. 9A shows a perspective view of a positron-emission tomography (PET)scanner, according to an embodiment of the present disclosure.

FIG. 9B shows a schematic view of a PET scanner, according to anembodiment of the present disclosure.

DETAILED DESCRIPTION

The following disclosure provides many different embodiments, orexamples, for implementing different features of the provided subjectmatter. Specific examples of components and arrangements are describedbelow to simplify the present disclosure. These are, of course, merelyexamples and are not intended to be limiting. For example, the formationof a first feature over or on a second feature in the description thatfollows may include embodiments in which the first and second featuresare formed in direct contact, and may also include embodiments in whichadditional features may be formed between the first and second features,such that the first and second features may not be in direct contact. Inaddition, the present disclosure may repeat reference numerals and/orletters in the various examples. This repetition is for the purpose ofsimplicity and clarity and does not in itself dictate a relationshipbetween the various embodiments and/or configurations discussed.Further, spatially relative terms, such as “top,” “bottom,” “beneath,”“below,” “lower,” “above,” “upper” and the like, may be used herein forease of description to describe one element or feature's relationship toanother element(s) or feature(s) as illustrated in the figures. Thespatially relative terms are intended to encompass differentorientations of the device in use or operation in addition to theorientation depicted in the figures. The apparatus may be otherwiseoriented (rotated 90 degrees or at other orientations) and the spatiallyrelative descriptors used herein may likewise be interpretedaccordingly.

The order of discussion of the different steps as described herein hasbeen presented for clarity sake. In general, these steps can beperformed in any suitable order. Additionally, although each of thedifferent features, techniques, configurations, etc. herein may bediscussed in different places of this disclosure, it is intended thateach of the concepts can be executed independently of each other or incombination with each other. Accordingly, the present invention can beembodied and viewed in many different ways.

In some imaging methods, three dimensional (3D) volumetric data sets canbe used to generate attenuation data, for example, diagnostic qualitydata sets as in computed tomography (CT) and magnetic resonance (MR).The method described herein augments (in the case of truncation) orreplaces (in the absence of) 3D volumetric scans with data obtained byprocessing two dimensional (2D) images (e.g. RGB, IR, etc.) or 3Dsurface scans (time of flight (ToF), radar, ultrasound, etc.).

The method described herein can include one or more visual imagingcameras (RGB, IR, etc.) that generate 2D images (e.g. RGB, IR, etc.) ofan object being imaged from various angles. In some embodiments, thecameras themselves can already be an integral part of the scannerimaging system (for patient monitoring, for example), in which case noadditional equipment is required. The 2D images can be processed todetermine locations of surfaces of objects of interest. In someembodiments, the method described herein can be implemented using camerasystems (time of flight, RADAR, structured light, etc.) which directlyprovide 3D surface scan information of the object.

FIG. 1 shows example reconstructions for an object 100. As previouslydescribed, without the attenuation map, the reconstructed image canexhibit artefacts that make the reconstruction harder to interpret. Theobject 100 may be scanned in, for example, a positron emissiontomography (PET) system. In an example where the object 100 issimplified as a 2D circle, even though the circular object 100 may beuniform in attenuation, the gamma rays originating at the edges of theobject 100 have less distance to travel through an attenuating medium toget to the detector as compared to gamma rays originating at the centerof the object 100. Gamma rays originating in the center of the object100 have to travel through one radius of the object 100 before they canmake their way to the detectors. Gamma rays originating at or near theedge of the object 100 have available some less attenuated paths to thedetectors and therefore appear brighter (higher energy). Thus, a firstreconstruction 105 of the object 100 without attenuation correction canbe non-uniform and exhibit artefacts, such as non-uniformity around theedge. A second reconstruction 110 of the object 100 with properattenuation correction can be uniform and not include artefacts. Thefirst reconstruction 105 can be based on the PET scan without anadditional scan to correct for the attenuation. The additional scan usedto correct the attenuation in the second reconstruction 110 can be via,for example, a volumetric CT scan. The PET data can provide functionalinformation about the tracer distribution in the object 100, but the PETdata is generally lower resolution and doesn't provide comprehensiveanatomical information about the object 100 (or patient). Thus, the CTscanning system can provide the high resolution anatomical informationfrom the attenuation data. When the two scans are used together duringanalysis, the accuracy of a diagnosis can increase due to the increasedinformation combined from the CT (anatomical attenuation) and PET(tracer distribution) scans.

However, as previously described, an imaging apparatus may not include asecondary volumetric scanning system to determine attenuation in theobject 100 via a volumetric scan, such as from the CT scanner system.Thus, the imaging apparatus may use a different secondary imaging systemto augment or replace the volumetric scanning system. In the case of aCT scanner system, it may be desirable to reduce the radiation dose on apatient, and therefore using a non-ionizing imaging system may be used.The non-ionizing imaging system can utilize wavelengths ofelectromagnetic radiation in the range of approximately 400 nm toseveral mm, wherein the wavelengths can not penetrate the patient orimpart tissue damage through ionization.

FIG. 2 shows a block diagram of an imaging apparatus 200, according toan embodiment of the present disclosure. In an embodiment, the imagingapparatus can include a primary imaging system 205, a secondary visualimaging system 210 (herein referred to as “visual imaging system 210”),and a volumetric imaging system 215. In an embodiment, the volumetricimaging system 215 can be replaced by the visual imaging system 210. Theprimary imaging system 205 can be, for example, the PET scanner asdescribed above. The volumetric imaging system 215 can be, for example,the CT scanner system as described above. The visual imaging system 210can be, for example, RGB vision cameras, stereoscopic cameras, infrared(IR) cameras, or structured light cameras, wherein structured lightcameras are configured to project light in a pattern (similar to amatrix) onto an object. The distortion/expansion of that pattern on thereflected image can provide a depth perception or contour. The visualimaging system 210 can be, for example, range-finding cameras such asRADAR, laser, LIDAR, and ultrasound, or any combination thereof. Forexample, the visual imaging system 210 can include multiple RGB visioncameras disposed at different locations in the imaging apparatus 200 inorder to image the patient at various angles. For example, the visualimaging system 210 can include multiple types of different imagingequipment, such as the RGB vision cameras in combination with the IR,RADAR, and LIDAR cameras. In general, the visual imaging system 210 caninclude imaging equipment configured to image the object withwavelengths of electromagnetic radiation within a range of approximately400 nm to several mm. The visual imaging system 210 can be already anintegrated part of the imaging apparatus 200, for example, for patientmonitoring. Thus, in such an example, no additional equipment needs tobe installed in the imaging apparatus 200. Other forms of the primaryimaging systems 205 and the volumetric imaging systems 215 can becontemplated by those skilled in the art.

Notably, the visual imaging system 210 can generate 2D visual images ofthe object or patient. Furthermore, in the case of multiple camerasbeing included, the generated 2D visual images can be from variousangles. The 2D visual images can be processed to determine locations ofsurfaces of objects of interest in order to generate an outline of theobject. The outline of the object can then be utilized to, for example,analyze a size of the object, assign material properties to differentportions of the object, and fit models to the object, as describedherein. Alternatively, the proposed solution may be implemented usingcamera systems that can directly provide 3D surface information, such asthe time of flight (ToF), RADAR, LIDAR, and structured light cameras.For example, a RADAR camera may provide the outline of the object basedon a generated 3D point cloud. For example, a structured light cameramay provide an outline of the object based on a projected known matrixof IR light. This may be especially effective in cases where thevolumetric imaging system 215 is not available in the imaging apparatus200 or not recommended for use. For example, volumetric imaging system215 may not be installed on prototype PET systems that do not includethe volumetric imaging system 215 yet. Other example cases have beencovered, such as when the CT (or MR) system may not be installed,available, or operational, when the CT scan may impart additionalundesirable radiation dose, and when the CT field of view (FOV) may notcover the entire object being scanned.

FIG. 3A shows a solid phantom 300 being outlined, according to anembodiment of the present disclosure. In an embodiment, the visualimaging system 210 can generate a 2D visual image (left) of the solidphantom 300 and infer a first outline 300 a of the solid phantom 300.Subsequently, an assessment can be made that the solid phantom 300 iscomposed of plastic and a corresponding attenuation value can beassigned to the first outline 300 a.

FIG. 3B shows a transparent phantom 305 being outlined, according to anembodiment of the present disclosure. In an embodiment, multipleobjects/materials can be detected and assigned attenuation values. Asshown, the transparent phantom 305 can be partially filled with aliquid, for example, water. The transparent phantom 305 itself can becomposed of plastic, and an unfilled portion for the transparent phantom305 can be air. Thus, the visual imaging system 210 can generate a 2Dvisual image (left) of the transparent phantom 305 and infer a secondoutline 305 a. The second outline 305 a can be decomposed into twoobjects: the plastic shell and the water. The second outline 305 a canbe segmented into two regions to yield an attenuation map with thetransparent phantom's 305 shape and material properties.

More broadly, images and/or scans of the object are obtained, preferablyfrom various angles, and the 2D image(s) or 3D surface scan from thevisual imaging system 210 can be processed to obtain the location ofsurfaces and the geometrical extent of the object. The camera can becalibrated with known fiducials to learn spatial geometry and scale.Existing libraries such as OpenCV can be used for this purpose. Usingfixed fiducial markers on the scanner can be used to translate pixelvalues to real units. The 3D shape of the object can then be evaluatedby segmenting the object in the 2D image(s) or 3D surface scan toidentify and extract relevant features. For example, the object can besegmented based on Hue Saturation Value (HSV) values. In anotherexample, a machine learning algorithm can be used to evaluate the 3Dshape of the object. The location and geometrical extent information canbe used in combination with a model of the object to generate theattenuation map of the object. Free parameters from the 2D visual imageor 3D surface scan information can be determined for the model, forexample, scaling, translation, and rotation. Subsequently, the 3Dvolumetric attenuation map is calculated based on the model and theemission tomography data is reconstructed with attenuation correctionbased on the 3D volumetric attenuation map to generate anattenuation-corrected 3D volumetric image. Here, the model can includemultiple types.

In an embodiment, a 3D computer aided design (CAD) model of the object(including known materials with known attenuation properties) can beused, wherein the free parameters can include a location and anorientation. This can be most applicable to imaging of rigid phantoms.An extension (described below) is the partially fillable phantom (thetransparent phantom 305) where another parameter describes the filllevel (or levels, if there are multiple fillable volumes).

In an embodiment, a library of pre-scanned CT or MR 3D volumes can beused. The free parameters can include a location, orientation, and scalefactor (i.e. determining translation, rotation, and scaling parametersto match the model to location information derived from the 2D images ofthe 3D surface scans). This can be most applicable to phantoms orpatients. To avoid truncation in the library, the library volumes couldbe obtained with “large bore” scanners. Patient couch attenuation datawould be included.

FIG. 4 shows an example of different size humans generated with XCAT,according to an embodiment of the present disclosure. In an embodiment,a 3D human digital model can be used, such as a Non-Uniform RationalB-Splines (NURBS)-based model. Examples of NURBS-based models includeXCAT, and MCAT, among others. In addition to translations and rotations,the free parameters can include external sizes of certain body parts(e.g. patient height, shoulder width, arm length, etc.) and angles ofjoints. From parameters of visible body parts, locations and sizes ofhidden parts or internal organs (e.g. bones, liver, lungs, etc.) can bedetermined based on most-probable values from population studies.

In an embodiment, with sufficient training, attenuation maps can begenerated by machine learning algorithms. The input can include 2Dimages or 3D surface scans, and the output can include 3D volumetricattenuation maps. For example, the training data can include thousandsof paired sets of 2D camera images and 3D volumetric attenuation maps.

FIG. 5 shows a non-limiting example of a flow chart for a method 500 ofgenerating a volumetric image corrected for attenuation, according to anembodiment of the present disclosure. Step 501 and step 505 can occurconcurrently. In step 501, emission tomography data is obtained via theprimary imaging system 205. For example, the imaging apparatus 200 canobtain projection data representing an intensity of radiation detectedalong a plurality of rays through the object. In step 505, 2D visualimage or 3D surface scan information is obtained via the visual imagingsystem 210. In step 510, relevant surface locations and orientations areidentified. In step 515, a model and the model parameters are determinedbased on the outline, relevant surface locations, and relevant surfaceorientations from the 2D visual image or 3D surface scan information. Instep 520, the 3D volumetric attenuation map is calculated based on themodel. In step 530, the emission tomography data is reconstructed withattenuation correction based on the 3D volumetric attenuation map togenerate an attenuation-corrected 3D volumetric image 535.

In an embodiment, the scanned object is a phantom and the visual imagingsystem 210 includes RGB vision cameras, and the method 500 can proceedas described herein. For imaging the phantom, the vision cameras arecalibrated against known fiducial markers and scales. A library isgenerated including multiple photos and CT attenuation data. The CTattenuation data can optionally be converted to 511 keV attenuation inthe library for PET. For fillable phantoms, fillable regions in theattenuation data can be identified, and attenuation maps can set thefillable regions to unfilled at the start. For the imaging apparatus 200being a PET scanner, the PET data and the vision camera images (i.e.photos) are obtained. From the vision camera images, the most likelyphantom from the library can be identified. In one example, machinelearning can be used to select the highest-likelihood phantom from thelibrary. The CT attenuation is registered via translating and rotatingto match the phantom in the vision camera images and an attenuation mapis generated. Optionally, for the phantom being a fillable phantom,image analysis can be used to identify a fill level of the materialinside the fillable phantom, and then fillable attenuation can be addedto the attenuation map. The emission tomography data (e.g. the PET orSPECT data) can be reconstructed using the generated attenuation map forattenuation correction. It may be appreciated that other types ofcameras for the visual imaging system 210 have been described and may beused in place of the RGB vision cameras to image the phantom.

In an embodiment, the scanned object is a human patient. The patientadds another factor that can be considered since the patient's clothingcan introduce errors when imaged via the RGB vision cameras. Namely,when the patient is wearing loose-fitting clothing or blankets, theoutline of the patient can become difficult to determine. In such acase, a semi-penetrating imaging modality can be used, such as RADAR,millimeter-wave scanners, etc. For the human patient, “unclothed” and“tightly clothed” regions in the optical images can be identified. Forexample, a “tightly clothed” region can include a shirt that isstretched across the patient's stomach or chest area. In one example,machine learning can be used to identify the regions. In anotherexample, a human user can use a GUI to “click” on and identify theregions, and then image analysis can enlarge or expand the “clicked”area to cover the entire region of interest. Parameters for a humanmodel can be generated based on the identified “unclothed” and “tightlyclothed” regions. For example, the parameters for the human model caninclude dimensions of body parts and angles of articulated limbs. Then,the attenuation map can be generated based on the human model, forexample, by using the XCAT digital phantom of FIG. 4.

FIG. 6A shows a FOV for a CT scanner, according to an embodiment of thepresent disclosure. FIG. 6B shows a FOV for a PET scanner, according toan embodiment of the present disclosure. In an embodiment, the FOV ofthe PET scanner can be often times larger than the FOV of the CT scannerin a PET/CT system. When objects extend beyond the CT's FOV, CTtruncation artifacts can arise. Concomitantly, these CT truncationartifacts can result in PET attenuation correction artifacts.

FIG. 6C shows an object in a PET/CT scanner with an object outside thescanner's FOV, according to an embodiment of the present disclosure. Inan embodiment, when an object (for example, the patient's arm) extendsbeyond the CT's FOV, images from the camera(s) in the visual imagingsystem 210 can be used to “fill in” the missing information. Forexample, the camera images can be used to generate parameters for thehuman model (e.g. dimensions of body parts, angles of articulated limbs,etc.). Within the CT's FOV, the (more accurate) CT images can be usedfor PET attenuation correction, and the augmented attenuation map fromthe cameras can be used outside the CT's FOV.

FIG. 7 shows a non-limiting example of a flow chart for a method 700 ofgenerating a volumetric image corrected for attenuation with truncated3D volumetric attenuation data, according to an embodiment of thepresent disclosure. The method 700 is similar to the method 500, butwith two optional paths shown and steps 727 and 729. As previouslydescribed, step 701 and step 705 can occur concurrently. In step 701,emission tomography data is obtained via the primary imaging system 205.In step 705, 2D visual image or 3D surface scan information is obtainedvia the visual imaging system 210. In step 710, relevant surfacelocations and orientations are identified. In step 715, a model and themodel parameters are determined based on the outline, relevant surfacelocations, and relevant surface orientations from the 2D visual image or3D surface scan information. Step 720 is also slightly modified. In step720, the 3D volumetric attenuation map for truncated regions iscalculated based on the model. In step 727, truncated 3D volumetricattenuation data is obtained via the volumetric imaging system 215. Forexample, the volumetric imaging system 215 can be the CT scanner or anMR scanner. Notably, an optional path connects step 727 with step 710,wherein the truncated 3D volumetric attenuation data can be combinedwith the 2D images/3D surface scan to produce a better ability tocorrectly identify relevant features. Another optional path connectsstep 727 with step 715, wherein the truncated 3D volumetric attenuationdata can be combined with the model parameters to improve estimation ofthe model parameters. For example, in the case where the modelparameters includes angles of articulated joints, if the patient iscovered by a bulky blanket, the truncated 3D volumetric attenuation datacan allow identification of the angle of a limb (within thenon-truncated region) which cannot be easily identified in the 2Dimages/3D surface scan. Having the better estimate of angle informationmight result in better estimation of the attenuation in the truncatedregion. In step 729, the obtained truncated 3D volumetric attenuationdata and the calculated data for the truncated regions can be merged. Instep 730, the emission tomography data is reconstructed with attenuationcorrection based on the 3D volumetric attenuation map (with calculateddata for the truncated regions) to generate an attenuation-corrected 3Dvolumetric image 735.

The methods 500 and 700 provide attenuation correction to reduceartefacts during reconstruction of the image of the patient.Advantageously, the methods provide: i) fast generation of vision images(RGB optical, IR, 3D surface contour); ii) minimization or completeelimination of time and cost-expensive transmission scans (via arotating line source or CT images); iii) radiation dose reduction whenobtaining the attenuation map for human patients; and iv) in-fill ofmissing or truncated parts of the scanned object due to truncated FOV ofCT or MR scanners. The following descriptions provide details for a CTscanner and a PET scanner separately, but it may be appreciated that thetwo scanners can be combined into a single imaging apparatus accordingto the embodiments described herein.

FIG. 8 shows a schematic of an implementation of a CT scanner accordingto an exemplary embodiment of the present disclosure. Referring to FIG.8, a radiography gantry 800 is illustrated from a side view and furtherincludes an X-ray tube 801, an annular frame 802, and a multi-row ortwo-dimensional-array-type X-ray detector 803. The X-ray tube 801 andX-ray detector 803 are diametrically mounted across an object OBJ on theannular frame 802, which is rotatably supported around a rotation axisRA (or an axis of rotation). A rotating unit 807 rotates the annularframe 802 at a high speed, such as 0.4 sec/rotation, while the objectOBJ is being moved along the axis RA into or out of the illustratedpage.

X-ray CT apparatuses include various types of apparatuses, e.g., arotate/rotate-type apparatus in which an X-ray tube and X-ray detectorrotate together around an object to be examined, and astationary/rotate-type apparatus in which many detection elements arearrayed in the form of a ring or plane, and only an X-ray tube rotatesaround an object to be examined. The present disclosure can be appliedto either type. The rotate/rotate type will be used as an example forpurposes of clarity.

The multi-slice X-ray CT apparatus further includes a high voltagegenerator 809 that generates a tube voltage applied to the X-ray tube801 through a slip ring 808 so that the X-ray tube 801 generates X-rays.The X-rays are emitted towards the object OBJ, whose cross sectionalarea is represented by a circle. For example, the X-ray tube 801 havingan average X-ray energy during a first scan that is less than an averageX-ray energy during a second scan. Thus, two or more scans can beobtained corresponding to different X-ray energies. The X-ray detector803 is located at an opposite side from the X-ray tube 801 across theobject OBJ for detecting the emitted X-rays that have transmittedthrough the object OBJ. The X-ray detector 803 further includesindividual detector elements or units.

The CT apparatus further includes other devices for processing thedetected signals from X-ray detector 803. A data acquisition circuit ora Data Acquisition System (DAS) 804 converts a signal output from theX-ray detector 803 for each channel into a voltage signal, amplifies thesignal, and further converts the signal into a digital signal. The X-raydetector 803 and the DAS 804 are configured to handle a predeterminedtotal number of projections per rotation (TPPR).

The above-described data is sent to a preprocessing device 806, which ishoused in a console outside the radiography gantry 800 through anon-contact data transmitter 805. The preprocessing device 806 performscertain corrections, such as sensitivity correction on the raw data. Amemory 812 stores the resultant data, which is also called projectiondata at a stage immediately before reconstruction processing. The memory812 is connected to a system controller 810 through a data/control bus811, together with a reconstruction device 814, input device 815, anddisplay 816. The system controller 810 controls a current regulator 813that limits the current to a level sufficient for driving the CT system.

The detectors are rotated and/or fixed with respect to the patient amongvarious generations of the CT scanner systems. In one implementation,the above-described CT system can be an example of a combinedthird-generation geometry and fourth-generation geometry system. In thethird-generation system, the X-ray tube 801 and the X-ray detector 803are diametrically mounted on the annular frame 802 and are rotatedaround the object OBJ as the annular frame 802 is rotated about therotation axis RA. In the fourth-generation geometry system, thedetectors are fixedly placed around the patient and an X-ray tuberotates around the patient. In an alternative embodiment, theradiography gantry 800 has multiple detectors arranged on the annularframe 802, which is supported by a C-arm and a stand.

The memory 812 can store the measurement value representative of theirradiance of the X-rays at the X-ray detector unit 803. Further, thememory 812 can store a dedicated program for executing, for example,various steps of the methods 110, 150, 200, and 300 for training aneural network and reducing imaging artifacts.

The reconstruction device 814 can execute various steps of the methods110, 150, 200, and 300. Further, reconstruction device 814 can executepre-reconstruction processing image processing such as volume renderingprocessing and image difference processing as needed.

The pre-reconstruction processing of the projection data performed bythe preprocessing device 806 can include correcting for detectorcalibrations, detector nonlinearities, and polar effects, for example.

Post-reconstruction processing performed by the reconstruction device814 can include filtering and smoothing the image, volume renderingprocessing, and image difference processing as needed. The imagereconstruction process can implement various of the steps of methods110, 150, 200, and 300 in addition to various CT image reconstructionmethods. The reconstruction device 814 can use the memory to store,e.g., projection data, reconstructed images, calibration data andparameters, and computer programs.

The reconstruction device 814 can include a CPU (processing circuitry)that can be implemented as discrete logic gates, as an ApplicationSpecific Integrated Circuit (ASIC), a Field Programmable Gate Array(FPGA) or other Complex Programmable Logic Device (CPLD). An FPGA orCPLD implementation may be coded in VHDL, Verilog, or any other hardwaredescription language and the code may be stored in an electronic memorydirectly within the FPGA or CPLD, or as a separate electronic memory.Further, the memory 812 can be non-volatile, such as ROM, EPROM, EEPROMor FLASH memory. The memory 812 can also be volatile, such as static ordynamic RAM, and a processor, such as a microcontroller ormicroprocessor, can be provided to manage the electronic memory as wellas the interaction between the FPGA or CPLD and the memory.

Alternatively, the CPU in the reconstruction device 814 can execute acomputer program including a set of computer-readable instructions thatperform the functions described herein, the program being stored in anyof the above-described non-transitory electronic memories and/or a harddisk drive, CD, DVD, FLASH drive or any other known storage media.Further, the computer-readable instructions may be provided as a utilityapplication, background daemon, or component of an operating system, orcombination thereof, executing in conjunction with a processor, such asa Xenon processor from Intel of America or an Opteron processor from AMDof America and an operating system, such as Microsoft VISTA, UNIX,Solaris, LINUX, Apple, MAC-OS and other operating systems known to thoseskilled in the art. Further, CPU can be implemented as multipleprocessors cooperatively working in parallel to perform theinstructions.

In one implementation, the reconstructed images can be displayed on adisplay 816. The display 816 can be an LCD display, CRT display, plasmadisplay, OLED, LED or any other display known in the art.

The memory 812 can be a hard disk drive, CD-ROM drive, DVD drive, FLASHdrive, RAM, ROM or any other electronic storage known in the art.

The PCDs can use a direct X-ray radiation detectors based onsemiconductors, such as cadmium telluride (CdTe), cadmium zinc telluride(CZT), silicon (Si), mercuric iodide (HgI₂), and gallium arsenide(GaAs). Semiconductor based direct X-ray detectors generally have muchfaster time response than indirect detectors, such as scintillatordetectors. The fast time response of direct detectors enables them toresolve individual X-ray detection events. However, at the high X-rayfluxes typical in clinical X-ray applications some pile-up of detectionevents will occur. The energy of a detected X-ray is proportional to thesignal generated by the direct detector, and the detection events can beorganized into energy bins yielding spectrally resolved X-ray data forspectral CT.

FIGS. 9A and 9B show a non-limiting example of a PET scanner 900 thatcan implement the methods 100 and 200. The PET scanner 900 includes anumber of gamma-ray detectors (GRDs) (e.g., GRD1, GRD2, through GRDN)that are each configured as rectangular detector modules. According toone implementation, the detector ring includes 40 GRDs. In anotherimplementation, there are 48 GRDs, and the higher number of GRDs is usedto create a larger bore size for the PET scanner 900.

Each GRD can include a two-dimensional array of individual detectorcrystals, which absorb gamma radiation and emit scintillation photons.The scintillation photons can be detected by a two-dimensional array ofphotomultiplier tubes (PMTs) that are also arranged in the GRD. A lightguide can be disposed between the array of detector crystals and thePMTs.

Alternatively, the scintillation photons can be detected by an array asilicon photomultipliers (SiPMs), and each individual detector crystalscan have a respective SiPM.

Each photodetector (e.g., PMT or SiPM) can produce an analog signal thatindicates when scintillation events occur, and an energy of the gammaray producing the detection event. Moreover, the photons emitted fromone detector crystal can be detected by more than one photodetector,and, based on the analog signal produced at each photodetector, thedetector crystal corresponding to the detection event can be determinedusing Anger logic and crystal decoding, for example.

FIG. 9B shows a schematic view of a PET scanner system having gamma-ray(gamma-ray) photon counting detectors (GRDs) arranged to detectgamma-rays emitted from an object OBJ. The GRDs can measure the timing,position, and energy corresponding to each gamma-ray detection. In oneimplementation, the gamma-ray detectors are arranged in a ring, as shownin FIGS. 9A and 9B. The detector crystals can be scintillator crystals,which have individual scintillator elements arranged in atwo-dimensional array and the scintillator elements can be any knownscintillating material. The PMTs can be arranged such that light fromeach scintillator element is detected by multiple PMTs to enable Angerarithmetic and crystal decoding of scintillation event.

FIG. 9B shows an example of the arrangement of the PET scanner 900, inwhich the object OBJ to be imaged rests on a table 916 and the GRDmodules GRD1 through GRDN are arranged circumferentially around theobject OBJ and the table 916. The GRDs can be fixedly connected to acircular component 920 that is fixedly connected to the gantry 940. Thegantry 940 houses many parts of the PET imager. The gantry 940 of thePET imager also includes an open aperture through which the object OBJand the table 916 can pass, and gamma-rays emitted in oppositedirections from the object OBJ due to an annihilation event can bedetected by the GRDs and timing and energy information can be used todetermine coincidences for gamma-ray pairs.

In FIG. 9B, circuitry and hardware is also shown for acquiring, storing,processing, and distributing gamma-ray detection data. The circuitry andhardware include: a processor 970, a network controller 974, a memory978, and a data acquisition system (DAS) 976. The PET imager alsoincludes a data channel that routes detection measurement results fromthe GRDs to the DAS 976, the processor 970, the memory 978, and thenetwork controller 974. The DAS 976 can control the acquisition,digitization, and routing of the detection data from the detectors. Inone implementation, the DAS 976 controls the movement of the bed 916.The processor 970 performs functions including reconstructing imagesfrom the detection data, pre-reconstruction processing of the detectiondata, and post-reconstruction processing of the image data, as discussedherein.

The processor 970 can be configured to perform various steps of methods100 and/or 200 described herein and variations thereof. The processor970 can include a CPU that can be implemented as discrete logic gates,as an Application Specific Integrated Circuit (ASIC), a FieldProgrammable Gate Array (FPGA) or other Complex Programmable LogicDevice (CPLD). An FPGA or CPLD implementation may be coded in VHDL,Verilog, or any other hardware description language and the code may bestored in an electronic memory directly within the FPGA or CPLD, or as aseparate electronic memory. Further, the memory may be non-volatile,such as ROM, EPROM, EEPROM or FLASH memory. The memory can also bevolatile, such as static or dynamic RAM, and a processor, such as amicrocontroller or microprocessor, may be provided to manage theelectronic memory as well as the interaction between the FPGA or CPLDand the memory.

Alternatively, the CPU in the processor 970 can execute a computerprogram including a set of computer-readable instructions that performvarious steps of method 100 and/or method 200, the program being storedin any of the above-described non-transitory electronic memories and/ora hard disk drive, CD, DVD, FLASH drive or any other known storagemedia. Further, the computer-readable instructions may be provided as autility application, background daemon, or component of an operatingsystem, or combination thereof, executing in conjunction with aprocessor, such as a Xenon processor from Intel of America or an Opteronprocessor from AMD of America and an operating system, such as MicrosoftVISTA, UNIX, Solaris, LINUX, Apple, MAC-OS and other operating systemsknown to those skilled in the art. Further, CPU can be implemented asmultiple processors cooperatively working in parallel to perform theinstructions.

The memory 978 can be a hard disk drive, CD-ROM drive, DVD drive, FLASHdrive, RAM, ROM or any other electronic storage known in the art.

The network controller 974, such as an Intel Ethernet PRO networkinterface card from Intel Corporation of America, can interface betweenthe various parts of the PET imager. Additionally, the networkcontroller 974 can also interface with an external network. As can beappreciated, the external network can be a public network, such as theInternet, or a private network such as an LAN or WAN network, or anycombination thereof and can also include PSTN or ISDN sub-networks. Theexternal network can also be wired, such as an Ethernet network, or canbe wireless such as a cellular network including EDGE, 3G, 4G, and 5Gwireless cellular systems. The wireless network can also be WiFi,Bluetooth, or any other wireless form of communication that is known.

In the preceding description, specific details have been set forth, suchas a particular geometry of a processing system and descriptions ofvarious components and processes used therein. It should be understood,however, that techniques herein may be practiced in other embodimentsthat depart from these specific details, and that such details are forpurposes of explanation and not limitation. Embodiments disclosed hereinhave been described with reference to the accompanying drawings.Similarly, for purposes of explanation, specific numbers, materials, andconfigurations have been set forth in order to provide a thoroughunderstanding. Nevertheless, embodiments may be practiced without suchspecific details. Components having substantially the same functionalconstructions are denoted by like reference characters, and thus anyredundant descriptions may be omitted.

Various techniques have been described as multiple discrete operationsto assist in understanding the various embodiments. The order ofdescription should not be construed as to imply that these operationsare necessarily order dependent. Indeed, these operations need not beperformed in the order of presentation. Operations described may beperformed in a different order than the described embodiment. Variousadditional operations may be performed and/or described operations maybe omitted in additional embodiments.

Those skilled in the art will also understand that there can be manyvariations made to the operations of the techniques explained abovewhile still achieving the same objectives of the invention. Suchvariations are intended to be covered by the scope of this disclosure.As such, the foregoing descriptions of embodiments of the invention arenot intended to be limiting. Rather, any limitations to embodiments ofthe invention are presented in the following claims.

What is claimed is:
 1. An imaging apparatus, comprising: processingcircuitry configured to obtain projection data for an objectrepresenting an intensity of radiation detected along a plurality ofrays through the object, obtain an outline of the object via a secondaryimaging system, the secondary imaging system using non-ionizingradiation, determine, based on the outline, a model and model parametersfor the object, calculate, based on the model and the model parameters,a volumetric attenuation map for the object, and reconstruct, based onthe projection data and the volumetric attenuation map, anattenuation-corrected volumetric image.
 2. The apparatus of claim 1,wherein the processing circuitry is further configured to obtain theoutline of the object by obtaining at least one 2D image of the object;identifying, via segmenting the object, locations and orientations ofsurfaces of the object; and determining, based on the locations andorientations of the surfaces of the object, the outline of the object.3. The apparatus of claim 2, wherein the processing circuitry is furtherconfigured to segment the object based on Hue Saturation Value (HSV)values.
 4. The apparatus of claim 2, wherein the secondary imagingsystem includes at least one optical input device.
 5. The apparatus ofclaim 2, wherein the secondary imaging system includes at least oneinfrared input device.
 6. The apparatus of claim 1, wherein theprocessing circuitry is further configured to obtain the outline of theobject by obtaining at least one 3D scan of the object; identifying, viasegmenting the object, locations and orientations of surfaces of theobject; and determining, based on the locations and orientations of thesurfaces of the object, the outline of the object.
 7. The apparatus ofclaim 6, wherein the secondary imaging system includes at least onerange-finding input device.
 8. The apparatus of claim 1, wherein themodel and model parameters are determined from a stored library.
 9. Theapparatus of claim 1, wherein the volumetric attenuation map for theobject includes truncated regions and the processing circuitry isfurther configured to obtain, via the secondary imaging system,truncated 3D volumetric attenuation data, and merge the truncated 3Dvolumetric attenuation data with the volumetric attenuation mapincluding truncated regions before reconstructing theattenuation-corrected volumetric image.
 10. The apparatus of claim 9,wherein the circuitry is further configured to combine the truncated 3Dvolumetric attenuation data with the outline of the object, and combinethe truncated 3D volumetric attenuation data with the model of theobject.
 11. A method of imaging, comprising: obtaining projection datafor an object representing an intensity of radiation detected along aplurality of rays through the object, obtaining an outline of the objectvia a secondary imaging system, the secondary imaging system usingnon-ionizing radiation, determining, based on the outline, a model andmodel parameters for the object, calculating, based on the model and themodel parameters, a volumetric attenuation map for the object, andreconstructing, based on the projection data and the volumetricattenuation map, an attenuation-corrected volumetric image.
 12. Themethod of claim 11, wherein the step of obtaining the outline of theobject further comprises: obtaining at least one 2D image of the object;identifying, via segmenting the object, locations and orientations ofsurfaces of the object; and determining, based on the locations andorientations of the surfaces of the object, the outline of the object.13. The method of claim 12, wherein the object is segmented based on HueSaturation Value (HSV) values.
 14. The method of claim 12, wherein thesecondary imaging system includes at least one optical input device. 15.The method of claim 11, wherein the step of obtaining the outline of theobject further comprises: obtaining at least one 3D scan of the object;identifying, via segmenting the object, locations and orientations ofsurfaces of the object; and determining, based on the locations andorientations of the surfaces of the object, the outline of the object.16. The method of claim 15, wherein the secondary imaging systemincludes at least one range-finding input device.
 17. The method ofclaim 11, wherein the model and model parameters are determined from astored library.
 18. The method of claim 11, wherein the volumetricattenuation map for the object includes truncated regions and the methodfurther comprises: obtaining, via the secondary imaging system,truncated 3D volumetric attenuation data; and merging the truncated 3Dvolumetric attenuation data with the volumetric attenuation mapincluding truncated regions before reconstructing theattenuation-corrected volumetric image.
 19. The method of claim 18,further comprising: combining the truncated 3D volumetric attenuationdata with the outline of the object; and combining the truncated 3Dvolumetric attenuation data with the model of the object.
 20. Anon-transitory computer-readable storage medium including executableinstructions, which when executed by circuitry, cause the circuitry toperform the method according to claim 11.